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1 <html>
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2 <head>
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3 <title>
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4 Netlab Reference Manual somtrain
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5 </title>
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6 </head>
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7 <body>
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8 <H1> somtrain
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9 </H1>
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10 <h2>
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11 Purpose
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12 </h2>
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13 Kohonen training algorithm for SOM.
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14
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15 <p><h2>
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16 Synopsis
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17 </h2>
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18 <PRE>
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19
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20 net = somtrain{net, options, x)
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21 </PRE>
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22
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23
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24 <p><h2>
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25 Description
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26 </h2>
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27 <CODE>net = somtrain{net, options, x)</CODE> uses Kohonen's algorithm to
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28 train a SOM. Both on-line and batch algorithms are implemented.
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29 The learning rate (for on-line) and neighbourhood size decay linearly.
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30 There is no error function minimised during training (so there is
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31 no termination criterion other than the number of epochs), but the
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32 sum-of-squares is computed and returned in <CODE>options(8)</CODE>.
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33
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34 <p>The optional parameters have the following interpretations.
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35
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36 <p><CODE>options(1)</CODE> is set to 1 to display error values; also logs learning
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37 rate <CODE>alpha</CODE> and neighbourhood size <CODE>nsize</CODE>.
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38 Otherwise nothing is displayed.
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39
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40 <p><CODE>options(5)</CODE> determines whether the patterns are sampled randomly
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41 with replacement. If it is 0 (the default), then patterns are sampled
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42 in order. This is only relevant to the on-line algorithm.
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43
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44 <p><CODE>options(6)</CODE> determines if the on-line or batch algorithm is
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45 used. If it is 1
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46 then the batch algorithm is used. If it is 0
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47 (the default) then the on-line algorithm is used.
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48
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49 <p><CODE>options(14)</CODE> is the maximum number of iterations (passes through
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50 the complete pattern set); default 100.
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51
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52 <p><CODE>options(15)</CODE> is the final neighbourhood size; default value is the
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53 same as the initial neighbourhood size.
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54
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55 <p><CODE>options(16)</CODE> is the final learning rate; default value is the same
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56 as the initial learning rate.
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57
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58 <p><CODE>options(17)</CODE> is the initial neighbourhood size; default 0.5*maximum
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59 map size.
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60
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61 <p><CODE>options(18)</CODE> is the initial learning rate; default 0.9. This parameter
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62 must be positive.
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63
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64 <p><h2>
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65 Examples
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66 </h2>
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67 The following example performs on-line training on a SOM in two stages:
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68 ordering and convergence.
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69 <PRE>
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70
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71 net = som(nin, [8, 7]);
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72 options = foptions;
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73
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74 <p>% Ordering phase
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75 options(1) = 1;
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76 options(14) = 50;
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77 options(18) = 0.9; % Initial learning rate
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78 options(16) = 0.05; % Final learning rate
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79 options(17) = 8; % Initial neighbourhood size
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80 options(15) = 1; % Final neighbourhood size
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81 net2 = somtrain(net, options, x);
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82
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83 <p>% Convergence phase
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84 options(14) = 400;
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85 options(18) = 0.05;
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86 options(16) = 0.01;
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87 options(17) = 0;
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88 options(15) = 0;
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89 net3 = somtrain(net2, options, x);
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90 </PRE>
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91
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92
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93 <p><h2>
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94 See Also
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95 </h2>
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96 <CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="som.htm">som</a></CODE>, <CODE><a href="somfwd.htm">somfwd</a></CODE><hr>
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97 <b>Pages:</b>
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98 <a href="index.htm">Index</a>
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99 <hr>
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100 <p>Copyright (c) Ian T Nabney (1996-9)
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101
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102
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103 </body>
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104 </html> |
